Eight components or eighty lines: two ways to keep an agent's secrets
A reader asked how our agent sandbox and secrets scheme compare to OneCLI, an open-source platform for running AI agents as a team. Fair question, and the answer is more interesting than “ours is better”, because on one axis theirs is, and checking the comparison turned up a sentence on our own product page that was not true.
What OneCLI is
A team control plane. One sandboxed agent per employee, provisioned from your identity provider, under a team policy, with shared connections, memory, scheduling, and a Slack identity each. It runs on your infrastructure. The runner is outbound-only with no inbound ports, which is the same idea our tunnel client uses to live behind NAT.
Tens of thousands of exposed AI agents - sandboxing must be the default
The always-on personal AI agent is the most exciting thing happening in computing right now - and the most exposed. Between late January and early February of this year, Bitsight’s internet-scale scanning observed more than 31,000 distinct exposed instances of the most popular personal-agent stack, listening on its default port across technology, healthcare, finance, government, and insurance networks. Independent scanning teams reported even larger numbers, including thousands of instances vulnerable to remote code execution.